Hippocampus segmentation in MR brain images using learned fuzzy mask and U-Net

The hippocampus is an important part of the human brain that is damaged in some diseases such as Alzheimer's, schizophrenia, and epilepsy. This paper presents a new method in hippocampus segmentation which is applicable in the early diagnosis of mentioned diseases. This method has introduced a two-section model to detect the hippocampus region in brain MR images. In the first section, the location of the hippocampus is roughly detected using a U-Net neural network model, and then a fuzzy mask is created around the detected area using a fuzzy function. In the second section, this mask is applied to the brain images and a U-Net neural network is used to segment these masked images, which finally predicts the location of the hippocampus. The main advantage and idea of this method is the use of a pre-trained fuzzy mask, which increases the quality of segmentation. The proposed method in this research was trained and tested using the HARP dataset, which contains 135 T1-weighted MRI volumes and the proposed model reached 0.95 dice in the best case.